1. Field of the Disclosure
The present disclosure relates computerized apparatus and methods for determining temporally persistent patterns in sensory input.
2. Description of Related Art
Object recognition in the context of computer vision relates to finding a given object in an image or a sequence of frames in a video segment. Typically, temporally proximate features that have high temporal correlations are identified within the sequence of frames, with successive frames containing temporally proximate representations of an object (persistent patterns). Object representations, also referred to as the “view”, may change from frame to frame due to a variety of object transformations, such as rotation, movement, translation, change in lighting, background, noise, appearance of other objects, partial blocking and/or unblocking of the object, and/or other object transformations. Temporally proximate object representations occur when the frame rate of object capture is commensurate with the timescales of these transformations, so that at least a subset of a particular object representation appears in several consecutive frames. Temporal proximity of object representations allows a computer vision system to recognize and associate different views with the same object (for example, different phases of a rotating triangle are recognized and associated with the same triangle). Such temporal processing (also referred to as learning) may enable object detection and tracking based on an invariant system response with respect to commonly appearing transformations (e.g., rotation, scaling, translation, and/or other commonly appearing transformations).
Some existing approaches to binding or associating temporarily proximate object features from different frames utilize artificial neuron networks (ANN). Accordingly, during operation such networks may not be able to accommodate changes of the temporally proximate features that were not present in the input during training.